The present invention relates to a method of determining a calibration curve and an automatic analyzer using this calibration curve. More particularly, the invention relates to a method of determining a calibration curve suitable for precisely measuring a limit value (cutoff value) to be used as a decision criterion in analyzing the components of a living organism, and an automatic analyzer using this calibration curve.
A conventional method of determining a calibration curve is known as disclosed in U.S. Pat. No. 3,998,591 and Japanese Patent Laid-open Publication JP-A-60-73436.
Instead of a straight calibration line conventionally used by the enzyme immunoassay (EIA) for chemical inspections at clinics, a calibration curve is generally used in testing immunoreaction through EIA. In addition, the shape of a calibration curve is susceptible to change with the type of measuring systems and reaction conditions. EIA is an assay for micro substance so that it is often performed near a limit value of detection, thus posing a problem of a relatively large variation of measured data of standard substances for respective concentrations. The theoretical formula of a calibration curve of EIA can be obtained if the antigen-antibody reaction based on which the measurement is carried out can be quantitatively analyzed. The formula is generally a complicated non-linear function which is very difficult to be dealt with statistically so that an empirical formula is often used. In either case, it becomes necessary to prepare a regression model to regress the calibration curve and solve the concentration of an unknown sample substance. As a regression model, there are known logistic curves, For instance, the following model is known: ##EQU1## where K=R.sub..infin. -R.sub.0,
R.sub.0 : a response for a standard substance (sample) with 0 (zero) concentration
R.sub..infin. : a response for a standard substance (sample) with infinite concentration
a, b: parameter
X(i): standard substance (sample)
Y(i): measured data (e.g., absorptivity data)
A conventional method of determining a calibration curve uses measured data per se of standard substances (samples). In other words, the conventional method determines a calibration curve without paying particular attention to the measured data near the cutoff value to be used in analyzing the components of living organism, in spite of the fact that the cutoff value plays an important role in diagnosing disease or pathology. Generally the average values of measured data of standard substances for respective concentrations are processed (through least-squares approach) to obtain the calibration curve.
Recently, high sensitivity immunoassays have been developed and the operation of measuring data is highly automated. For instance, substances associated with infectious disease can now be automatically measured contrary to the conventional manual operation. Different from an ordinary quantitative measurement, immunoassays aim at a qualitative measurement through which it is decided if an object substance is present in a specimen. For instance, it is checked if there is an antibody of HIV (i.e., AIDS) to decide whether or not the patient is infected with AIDS. Taking as an example a cancer marker AFP (.alpha.-fetoprotein) commonly undergoing a quantitative measurement, for a screening test, to check the measured AFP value itself is not as important as to check if the measured AFP value falls within the range of values for a normal person or for a cancer patient. A limit value for such decision criterion is called a cutoff value. In order to reliably decide whether a person is infected with AIDS or cancer if the measured value of a specimen is higher than a cutoff value, and not infected if it is lower than the cutoff value, the data measured near the cutoff value are required to be more precise than the data for other concentrations. It is also necessary to use a calibration curve which well matches the concentrations at the concentration range near the cutoff value than at the other concentration ranges.
The conventional method of determining a calibration curve, however, processes a plurality of measured data of standard substances without weighting the data in a particular concentration range, for example, the data near the cutoff value, to thus make the calibration curve well match measured data. The conventional method takes the necessary measures for reducing the adverse effects of data variation by increasing the number of measurements of a standard substance near the cutoff value and using the average value thereof. However, in determining the calibration curve, the average values of the measured data near the cutoff value are processed in the similar manner as the average values at the other concentrations, without taking into consideration weighting the data near the cutoff value.